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A network analysis to identify mediators of germline-driven differences in breast cancer prognosis

Escala-Garcia, Maria ; Brenner, Hermann LU ; Ellberg, Carolina LU ; Olsson, Håkan LU ; Hall, Per LU ; Canisius, Sander and Schmidt, Marjanka K (2020) In Nature Communications 11(1).
Abstract
Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as... (More)
Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis. © 2020, The Author(s). (Less)
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type
Contribution to journal
publication status
published
subject
in
Nature Communications
volume
11
issue
1
article number
312
publisher
Nature Publishing Group
external identifiers
  • scopus:85077940819
  • pmid:31949161
ISSN
2041-1723
DOI
10.1038/s41467-019-14100-6
language
English
LU publication?
yes
id
e52ae25c-0b44-4f16-a168-4e108614c605
date added to LUP
2020-01-27 14:04:41
date last changed
2020-03-16 10:47:26
@article{e52ae25c-0b44-4f16-a168-4e108614c605,
  abstract     = {Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis. © 2020, The Author(s).},
  author       = {Escala-Garcia, Maria and Brenner, Hermann and Ellberg, Carolina and Olsson, Håkan and Hall, Per and Canisius, Sander and Schmidt, Marjanka K},
  issn         = {2041-1723},
  language     = {eng},
  number       = {1},
  publisher    = {Nature Publishing Group},
  series       = {Nature Communications},
  title        = {A network analysis to identify mediators of germline-driven differences in breast cancer prognosis},
  url          = {http://dx.doi.org/10.1038/s41467-019-14100-6},
  doi          = {10.1038/s41467-019-14100-6},
  volume       = {11},
  year         = {2020},
}